Cluster diagnostics data for distributed job execution

ABSTRACT

A shared database platform can interface with a cluster computing platform over a network through a database connector and one or more cluster connectors. The data transferred over the network can include telemetry metadata that can be distributed to execution nodes of the cluster computing platform for generation and transmission of cluster data to the shared database platform.

PRIORITY APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.17/399,847 filed Aug. 11, 2021, which is a Continuation of U.S. patentapplication Ser. No. 17/218,277, filed on Mar. 31, 2021 and issued onSep. 7, 2021 as U.S. Pat. No. 11,113,151, which is a Continuation ofU.S. patent application Ser. No. 17/161,989, filed Jan. 29, 2021 andissued on Jun. 8, 2021 as U.S. Pat. No. 11,030,046, which claims thebenefit of priority to U.S. Provisional Application Ser. No. 63/136,341,filed Jan. 12, 2021, the disclosures of which are incorporated herein byreference in their entireties.

TECHNICAL FIELD

The present disclosure generally relates to special-purpose machinesthat manage databases and improvements to such variants and to thetechnologies by which such special-purpose machines become improvedcompared to other special-purpose machines for managing diagnostic datafor distributed job execution.

BACKGROUND

Distributed processing can be used to create analytical compute sourcesto analyze data. Some of these distributed computing systems include acluster of nodes including a driver node and multiple execution nodesthat function in concert per the driver node's instructions to completedata processing tasks. While these distributed systems enable powerfulcomputing, inefficiencies such as bottlenecks can still occur.Furthermore, the logging and diagnostic information for the cluster ofnodes is complicated and inconvenient for end-users because there aremany various node cluster versions, and the end-user (e.g., customer)may have no experience nor access privilege to collect the informationfor diagnosis and tracking of processing in the distributed nodecluster.

BRIEF DESCRIPTION OF THE DRAWINGS

Various ones of the appended drawings merely illustrate exampleembodiments of the present disclosure and should not be considered aslimiting its scope.

FIG. 1 illustrates an example computing environment in which anetwork-based data warehouse system can implement cluster computingusing a metadata connector, in accordance with some embodiments of thepresent disclosure.

FIG. 2 is a block diagram illustrating components of a compute servicemanager, in accordance with some embodiments of the present disclosure.

FIG. 3 is a block diagram illustrating components of an executionplatform, in accordance with some embodiments of the present disclosure.

FIG. 4 shows an example cluster computing dataflow architectureimplementing the cluster diagnostic system, in accordance with someembodiments of the present disclosure.

FIG. 5 shows a flow diagram of a method for implementing a clusterdiagnosis system, in accordance with some embodiments of the presentdisclosure.

FIG. 6 illustrates a diagrammatic representation of a machine in theform of a computer system within which a set of instructions may beexecuted for causing the machine to perform any one or more of themethodologies discussed herein, in accordance with some embodiments ofthe present disclosure.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail

As discussed, enabling logging and diagnostic information fordistributed clusters of nodes can be problematic. In some exampleembodiments, if the end-user encounters an issue or an error, the userthen (1) reproduces the error with an additional option on thediagnostic system set as “enable_diag=true”, and then, (2) while thisoption is set to true, log data from with a unique applicationidentifier (ID) output after the cluster job (e.g., Apache Spark job).The user can then send the unique ID with cluster data to a networkdatabase for analysis by administrators of the database to furtherinvestigate the issue. In the below examples, Apache Spark is discussedas the distributed node cluster that can connect to a distributed datawarehouse system through a cluster connector. In some exampleembodiments, the Spark connector runs as a Spark cluster process and thespark connector plug-in is configured to connect to the distributed datawarehouse system using a database connection, such as JDBC. AlthoughApache Spark and Java Database Connectivity (JDBC) connector interfacesare discussed here as examples, it is appreciated that other types ofcluster computing environments and database connectors can beimplemented in a similar manner. A spark connector runs in thedistributed Spark cluster environment (e.g., external to the databaseenvironment). If an end-user encounters some issues or errors, thedatabase administrators of the distributed database generally need tohelp the end-user diagnose the problem. The diagnosis for distributedcluster computing systems that are connected to distributed databasesover a network is complex and inconvenient, and can require collectionof the following: (1) what version of the cluster of nodes (e.g., whatversion of Spark), what version of the spark connector plug-in, whatScala (programming language) version, and what application or jobversion was submitted, (2) what distributed database JDBC version, (3)what type or configuration of cluster has been implemented, (4) whattype of distributed database user account the end-user is using, (5)whether a stack-trace has occurred and if so, what was the result, (6)whether any error messages have been generated, and if so, what errors,(7) whether there are cluster connector logs available (e.g., logs thatinclude spark driver and spark executors' log) (8) the Spark clusteroperational parameters and configuration (e.g., total memory, centralprocessing unit core count, etc.), (9) and/or estimated data size fordataframe and spark partition.

Requiring collection of the above data by the end-user is generallycomplex and can frustrate the users, which causes a very poor userexperience. It is typically very difficult for users to collect item (7)(spark connector logs) because there are many kinds of spark clusters,such as on-premise cluster, on-premise cluster on cloud infrastructure,yarn cluster, k8s cluster, DataBrick, Amazon EMR, Qubole, etc., and eachrequires different operations and knowledge to perform the datacollections.

In contrast, with the Cluster Diagnostic System implemented, when thecustomer meets an issue, the end-user then: (1) reproduces the issuewith an additional option activated (e.g., “enable_diag=true”), afterwhich (2) log data and a unique ID are generated and sent to thedatabase. The database administrators can then analyze and query the logdata and help investigate based on the unique ID of the distributedcluster system.

FIG. 1 illustrates an example shared data processing platform 100 inwhich a network-based data warehouse system 102 functions as a datasource for a cluster computing platform 150 connected by way of adatabase connector interface, such as an application programminginterface (API) connector 145, in accordance with some embodiments ofthe present disclosure. To avoid obscuring the inventive subject matterwith unnecessary detail, various functional components that are notgermane to conveying an understanding of the inventive subject matterhave been omitted from the figures. However, a skilled artisan willreadily recognize that various additional functional components may beincluded as part of the shared data processing platform 100 tofacilitate additional functionality that is not specifically describedherein.

As shown, the shared data processing platform 100 comprises thenetwork-based data warehouse system 102, a cloud computing storageplatform 104 (e.g., a storage platform, an AWS® service such as S3,Microsoft Azure®, or Google Cloud Services®), and a user device 106. Thenetwork-based data warehouse system 102 is a network-based system usedfor storing and accessing data (e.g., internally storing data, accessingexternal remotely located data) in a multitenant integrated manner, andreporting and analysis of the integrated data from the one or moredisparate sources (e.g., the cloud computing storage platform 104),where additional analytical computing can be performed by the clustercomputing platform 150. The cloud computing storage platform 104comprises a plurality of computing machines and provides on-demandcomputer system resources such as data storage and computing power tothe network-based data warehouse system 102.

The user device 106 (e.g., a user device such as a laptop computer)comprises one or more computing machines (e.g., a client device such asa laptop computer) that execute a software component 108 (e.g., browseraccessed cloud service, native app such as a mobile app for a mobileoperating system) to provide additional functionality to users of thenetwork-based data warehouse system 102.

The software component 108 comprises a set of machine-readableinstructions (e.g., code) that, when executed by the user device 106,cause the user device 106 to provide certain functionality. The softwarecomponent 108 may operate on input data and generate result data basedon processing, analyzing, or otherwise transforming the input data. Asan example, the software component 108 can be a browser that accesses acloud run customer application on the cluster computing platform 150 forcomputation by a driver node 152 and executor nodes 154-157, asdiscussed in further detail below.

The network-based data warehouse system 102 comprises an accessmanagement system 110, a compute service manager 112, an executionplatform 114, and a database 116. The access management system 110enables administrative users to manage access to resources and servicesprovided by the network-based data warehouse system 102. Administrativeusers can create and manage users, roles, and groups, and usepermissions to allow or deny access to resources and services. Theaccess management system 110 can store share data that securely managesshared access to the storage resources of the cloud computing storageplatform 104 amongst different users of the network-based data warehousesystem 102, as discussed in further detail below.

The compute service manager 112 coordinates and manages operations ofthe network-based data warehouse system 102. The compute service manager112 also performs query optimization and compilation as well as managingclusters of computing services that provide compute resources (e.g.,virtual warehouses, virtual machines, application container clusters) inthe execution platform 114. The compute service manager 112 can supportany number of client accounts such as end users providing data storageand retrieval requests, system administrators managing the systems andmethods described herein, and other components/devices that interactwith compute service manager 112.

The compute service manager 112 is also coupled to database 116, whichis associated with the entirety of the data managed by the shared dataprocessing platform 100. The database 116 stores data pertaining tovarious functions and aspects associated with the network-based datawarehouse system 102 and its users. For example, data against whichqueries can be executed by a customer application running on clustercomputing platform 150 can be stored in database 116 as internal data orin a storage platform 122 as external data.

In some embodiments, database 116 includes a summary of data stored inremote data storage systems as well as data available from one or morelocal caches. Additionally, database 116 may include informationregarding how data is organized in the remote data storage systems andthe local caches. Database 116 allows systems and services to determinewhether a piece of data needs to be accessed without loading oraccessing the actual data from a storage device. The compute servicemanager 112 is further coupled to an execution platform 114, whichprovides multiple computing resources (e.g., virtual warehouses) thatexecute various data storage and data retrieval tasks, as discussed ingreater detail below.

Execution platform 114 is coupled to multiple data storage devices 124-1to 124-n that are part of a cloud computing storage platform 104.Although two data storage devices 124-1 and 124-n are shown in FIG. 1,execution platform 114 is capable of communicating with any number ofdata storage devices as part of an elastic storage system. In someembodiments, data storage devices 124-1 to 124-n are cloud-based storagedevices located in one or more geographic locations. For example, datastorage devices 124-1 to 124-n may be part of a public cloudinfrastructure or a private cloud infrastructure. Data storage devices124-1 to 124-n may be hard disk drives (HDDs), solid state drives(SSDs), storage clusters, Amazon S3 storage systems or any other datastorage technology. Additionally, cloud computing storage platform 104may include distributed file systems (such as Hadoop Distributed FileSystems (HDFS)), object storage systems, and the like.

The execution platform 114 comprises a plurality of compute nodes (e.g.,virtual warehouses). A set of processes on a compute node executes aquery plan compiled by the compute service manager 112. The set ofprocesses can include: a first process to execute the query plan; asecond process to monitor and delete micro-partition files using a leastrecently used (LRU) policy, and implement an out of memory (OOM) errormitigation process; a third process that extracts health informationfrom process logs and status information to send back to the computeservice manager 112; a fourth process to establish communication withthe compute service manager 112 after a system boot; and a fifth processto handle all communication with a compute cluster for a given jobprovided by the compute service manager 112 and to communicateinformation back to the compute service manager 112 and other computenodes of the execution platform 114.

The cloud computing storage platform 104 also comprises an accessmanagement system 118 and a cloud interface 120 (e.g., API gateway forcloud computing storage platform 104). As with the access managementsystem 110, the access management system 118 allows users to create andmanage users, roles, and groups, and use permissions to allow or denyaccess to cloud services and resources. The access management system 110of the network-based data warehouse system 102 and the access managementsystem 118 of the cloud computing storage platform 104 can communicateand share information so as to enable access and management of resourcesand services shared by users of both the network-based data warehousesystem 102 and the cloud computing storage platform 104. The cloudinterface 120 handles tasks involved in accepting and processingconcurrent API calls, including traffic management, authorization andaccess control, monitoring, and API version management. The cloudinterface 120 provides Hypertext Transfer Protocol (HTTP) proxy servicefor creating, publishing, maintaining, securing, and monitoring APIs(e.g., Representational State Transfer (REST) APIs).

In some embodiments, communication links between elements of the shareddata processing platform 100 are implemented via one or more datacommunication networks. These data communication networks may utilizeany communication protocol and any type of communication medium. In someembodiments, the data communication networks are a combination of two ormore data communication networks (or sub-networks) coupled to oneanother. In alternate embodiments, these communication links areimplemented using any type of communication medium and any communicationprotocol.

As shown in FIG. 1, data storage devices 124-1 to 124-N are decoupledfrom the computing resources associated with the execution platform 114.That is, new virtual warehouses can be created and terminated in theexecution platform 114 and additional data storage devices can becreated and terminated on the cloud computing storage platform 104 in anindependent manner (e.g., cloud computing storage platform 104 is anexternal network platform, such as Amazon AWS, separately managed butlinked to network-based data warehouse system 102). This architecturesupports dynamic changes to the network-based data warehouse system 102based on the changing data storage/retrieval needs as well as thechanging needs of the users and systems accessing the shared dataprocessing platform 100. The support of dynamic changes allows thenetwork-based data warehouse system 102 to scale quickly in response tochanging demands on the systems and components within the network-baseddata warehouse system 102. The decoupling of the computing resourcesfrom the data storage devices supports the storage of large amounts ofdata without requiring a corresponding large amount of computingresources. Similarly, this decoupling of resources supports asignificant increase in the computing resources utilized at a particulartime without requiring a corresponding increase in the available datastorage resources. Additionally, the decoupling of resources enablesdifferent accounts to handle creating additional compute resources toprocess data shared by other users without affecting the other users'systems. For instance, a data provider may have three compute resourcesand share data with a data consumer, and the data consumer may generatenew compute resources to execute queries against the shared data, wherethe new compute resources are managed by the data consumer and do notaffect or interact with the compute resources of the data provider.

The cluster computing platform 150 is a cluster computing environmentthat can extend the computing analysis of the network-based datawarehouse system 102. For example, whereas the network-based datawarehouse system 102 can be configured to function with the cloudcomputing storage platform 104 to enable a decoupled data warehouse thatcan scale, the cluster computing platform 150 can be a big data orno-SQL platform (e.g., Apache Spark, Hadoop, Cassandra) that implementsa driver node 152 and a plurality of executor nodes 154-157 to performdistributed computing tasks (e.g., data analysis). In some exampleembodiments, the network-based data warehouse system 102 and the cloudcomputing storage platform 104 function as a single entity, and thecluster computing platform 150 is agnostic to the decoupling andfunctions of the single entity. For instance, the network-based datawarehouse system 102 and the cloud computing storage platform 104 can beimplemented as a Snowflake data source to an Apache Spark Cluster (e.g.,an example embodiment of the cluster computing platform 150), where thetwo platforms are connected via API connector 145 such as JDBC. Althoughthe API connector 145 is shown between the network-based data warehousesystem 102 and the cluster computing platform 150, it is appreciatedthat the API connector 145 can be integrated within the network-baseddata warehouse system 102, as discussed in further detail below withreference to FIG. 2.

Further, compute service manager 112, database 116, execution platform114, cloud computing storage platform 104, cluster computing platform150, and user device 106 are shown in FIG. 1 as individual components.However, each of compute service manager 112, database 116, executionplatform 114, cloud computing storage platform 104, and clustercomputing platform 150 may be implemented as a distributed system (e.g.,distributed across multiple systems/platforms at multiple geographiclocations) connected by APIs and access information (e.g., tokens, logindata). Additionally, each of compute service manager 112, database 116,execution platform 114, and cloud computing storage platform 104 can bescaled up or down (independently of one another) depending on changes tothe requests received and the changing needs of shared data processingplatform 100. Thus, in the described embodiments, the network-based datawarehouse system 102 is dynamic and supports regular changes to meet thecurrent data processing needs.

During typical operation, the network-based data warehouse system 102processes multiple jobs (e.g., queries from cluster computing platform150) determined by the compute service manager 112. These jobs arescheduled and managed by the compute service manager 112 to determinewhen and how to execute the job. For example, the compute servicemanager 112 may divide the job into multiple discrete tasks and maydetermine what data is needed to execute each of the multiple discretetasks. The compute service manager 112 may assign each of the multiplediscrete tasks to one or more nodes of the execution platform 114 toprocess the task. The compute service manager 112 may determine whatdata is needed to process a task and further determine which nodeswithin the execution platform 114 are best suited to process the task.Some nodes may have already cached the data needed to process the task(due to the nodes having recently downloaded the data from the cloudcomputing storage platform 104 for a previous job) and, therefore, maybe a good candidate for processing the task. Metadata stored in thedatabase 116 assists the compute service manager 112 in determiningwhich nodes in the execution platform 114 have already cached at least aportion of the data needed to process the task. One or more nodes in theexecution platform 114 process the task using data cached by the nodesand, if necessary, data retrieved from the cloud computing storageplatform 104. It is desirable to retrieve as much data as possible fromcaches within the execution platform 114 because the retrieval speed istypically much faster than retrieving data from the cloud computingstorage platform 104.

As shown in FIG. 1, the shared data processing platform 100 separatesthe execution platform 114 from the cloud computing storage platform104. In this arrangement, the processing resources and cache resourcesin the execution platform 114 operate independently of the data storagedevices 124-1 to 124-n in the cloud computing storage platform 104.Thus, the computing resources and cache resources are not restricted tospecific data storage devices 124-1 to 124-n. Instead, all computingresources and all cache resources may retrieve data from, and store datato, any of the data storage resources in the cloud computing storageplatform 104.

FIG. 2 is a block diagram illustrating components of the compute servicemanager 112, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 2, a request processing service 202 managesreceived data storage requests and data retrieval requests (e.g., jobsto be performed on database data). For example, the request processingservice 202 may determine the data necessary to process a received query(e.g., a data storage request or data retrieval request). The data maybe stored in a cache within the execution platform 114 or in a datastorage device in cloud computing storage platform 104. A managementconsole service 204 supports access to various systems and processes byadministrators and other system managers. Additionally, the managementconsole service 204 may receive a request to execute a job and monitorthe workload on the system.

The result object manager 207 is configured to generate serializedresult files for storage on a staging platform and generate the objectmetadata set, which is a metadata list describing the result filesstored in the staging platform. The result object manager 207 includesthe API connector 145 as a relational database connection interface forfacilitating data transfers (e.g., receiving queries and transmittingresult data) between the network-based data warehouse system 102 and thecluster computing platform 150. For example, a customer applicationrunning on cluster computing platform 150 can issue a query to thenetwork-based data warehouse system 102, which is directed to the APIconnector 145 for parsing and forwarding as a job request to the requestprocessing service. Although the API connector 145 is illustrated asbetween the cluster computing platform 150 and the network-based datawarehouse system 102, in some example embodiments the API connector 145is installed in the network-based data warehouse system 102 to send andreceive data to the cluster computing platform 150, which may be anexternally run cluster computing platform 150 managed by a differentcompany (e.g., cluster computing platform 150 can be an Apache Sparkcluster hosted by the Databricks® platform or other Spark platforms).

The compute service manager 112 also includes a job compiler 206, a joboptimizer 208, and a job executor 210. The job compiler 206 parses a jobinto multiple discrete tasks and generates the execution code for eachof the multiple discrete tasks. The job optimizer 208 determines thebest method to execute the multiple discrete tasks based on the datathat needs to be processed. The job optimizer 208 also handles variousdata pruning operations and other data optimization techniques toimprove the speed and efficiency of executing the job. The job executor210 executes the execution code for jobs received from a queue ordetermined by the compute service manager 112.

A job scheduler and coordinator 212 sends received jobs to theappropriate services or systems for compilation, optimization, anddispatch to the execution platform 114. For example, jobs may beprioritized and then processed in that prioritized order. In anembodiment, the job scheduler and coordinator 212 determines a priorityfor internal jobs that are scheduled by the compute service manager 112with other “outside” jobs such as user queries that may be scheduled byother systems in the database but may utilize the same processingresources in the execution platform 114. In some embodiments, the jobscheduler and coordinator 212 identifies or assigns particular nodes inthe execution platform 114 to process particular tasks. A virtualwarehouse manager 214 manages the operation of multiple virtualwarehouses implemented in the execution platform 114. As discussedbelow, each virtual warehouse includes multiple execution nodes thateach include a cache and a processor (e.g., a virtual machine, anoperating system level container execution environment).

Additionally, the compute service manager 112 includes a configurationand metadata manager 216, which manages the information related to thedata stored in the remote data storage devices and in the local caches(i.e., the caches in execution platform 114). The configuration andmetadata manager 216 uses the metadata to determine which datamicro-partitions need to be accessed to retrieve data for processing aparticular task or job. A monitor and workload analyzer 218 overseesprocesses performed by the compute service manager 112 and manages thedistribution of tasks (e.g., workload) across the virtual warehouses andexecution nodes in the execution platform 114. The monitor and workloadanalyzer 218 also redistributes tasks, as needed, based on changingworkloads throughout the network-based data warehouse system 102 and mayfurther redistribute tasks based on a user (e.g., “external”) queryworkload that may also be processed by the execution platform 114. Theconfiguration and metadata manager 216 and the monitor and workloadanalyzer 218 are coupled to a data storage device 220. Data storagedevice 220 in FIG. 2 represents any data storage device within thenetwork-based data warehouse system 102. For example, data storagedevice 220 may represent caches in execution platform 114, storagedevices in cloud computing storage platform 104, or any other storagedevice.

FIG. 3 is a block diagram illustrating components of the executionplatform 114, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 3, execution platform 114 includes multiplevirtual warehouses, which are elastic clusters of compute instances,such as virtual machines. In the example illustrated, the virtualwarehouses include virtual warehouse 1, virtual warehouse 2, and virtualwarehouse n. Each virtual warehouse (e.g., EC2 cluster) includesmultiple execution nodes (e.g., virtual machines) that each include adata cache and a processor. The virtual warehouses can execute multipletasks in parallel by using the multiple execution nodes. As discussedherein, execution platform 114 can add new virtual warehouses and dropexisting virtual warehouses in real-time based on the current processingneeds of the systems and users. This flexibility allows the executionplatform 114 to quickly deploy large amounts of computing resources whenneeded without being forced to continue paying for those computingresources when they are no longer needed. All virtual warehouses canaccess data from any data storage device (e.g., any storage device incloud computing storage platform 104).

Although each virtual warehouse shown in FIG. 3 includes three executionnodes, a particular virtual warehouse may include any number ofexecution nodes. Further, the number of execution nodes in a virtualwarehouse is dynamic, such that new execution nodes are created whenadditional demand is present, and existing execution nodes are deletedwhen they are no longer necessary (e.g., upon a query or jobcompletion).

Each virtual warehouse is capable of accessing any of the data storagedevices 124-1 to 124-n shown in FIG. 1. Thus, the virtual warehouses arenot necessarily assigned to a specific data storage device 124-1 to124-n and, instead, can access data from any of the data storage devices124-1 to 124-n within the cloud computing storage platform 104.Similarly, each of the execution nodes shown in FIG. 3 can access datafrom any of the data storage devices 124-1 to 124-n. For instance, thestorage device 124-1 of a first user (e.g., provider account user) maybe shared with an executor node in a virtual warehouse of another user(e.g., consumer account user), such that the another user can create adatabase (e.g., read-only database) and use the data in storage device124-1 directly without needing to copy the data (e.g., copy it to a newdisk managed by the consumer account user). In some embodiments, aparticular virtual warehouse or a particular execution node may betemporarily assigned to a specific data storage device, but the virtualwarehouse or execution node may later access data from any other datastorage device.

In the example of FIG. 3, virtual warehouse 1 includes three executionnodes 302-1, 302-2, and 302-n. Execution node 302-1 includes a cache304-1 and a processor 306-1. Execution node 302-2 includes a cache 304-2and a processor 306-2. Execution node 302-n includes a cache 304-n and aprocessor 306-n. Each execution node 302-1, 302-2, and 302-n isassociated with processing one or more data storage and/or dataretrieval tasks. For example, a virtual warehouse may handle datastorage and data retrieval tasks associated with an internal service,such as a clustering service, a materialized view refresh service, afile compaction service, a storage procedure service, or a file upgradeservice. In other implementations, a particular virtual warehouse mayhandle data storage and data retrieval tasks associated with aparticular data storage system or a particular category of data.

Similar to virtual warehouse 1 discussed above, virtual warehouse 2includes three execution nodes 312-1, 312-2, and 312-n. Execution node312-1 includes a cache 314-1 and a processor 316-1. Execution node 312-2includes a cache 314-2 and a processor 316-2. Execution node 312-nincludes a cache 314-n and a processor 316-n. Additionally, virtualwarehouse 3 includes three execution nodes 322-1, 322-2, and 322-n.Execution node 322-1 includes a cache 324-1 and a processor 326-1.Execution node 322-2 includes a cache 324-2 and a processor 326-2.Execution node 322-n includes a cache 324-n and a processor 326-n.

In some embodiments, the execution nodes shown in FIG. 3 are statelesswith respect to the data being cached by the execution nodes. Forexample, these execution nodes do not store or otherwise maintain stateinformation about the execution node, or the data being cached by aparticular execution node. Thus, in the event of an execution nodefailure, the failed node can be transparently replaced by another node.Since there is no state information associated with the failed executionnode, the new (replacement) execution node can easily replace the failednode without concern for recreating a particular state.

Although the execution nodes shown in FIG. 3 each include one data cacheand one processor, alternate embodiments may include execution nodescontaining any number of processors and any number of caches.Additionally, the caches may vary in size among the different executionnodes. The caches shown in FIG. 3 store, in the local execution node(e.g., local disk), data that was retrieved from one or more datastorage devices in cloud computing storage platform 104 (e.g., S3objects recently accessed by the given node). In some exampleembodiments, the cache stores file headers, and individual columns offiles as a query downloads only columns necessary for that query.

To improve cache hits and avoid overlapping redundant data stored in thenode caches, the job optimizer 208 assigns input file sets to the nodesusing a consistent hashing scheme to hash over table file names of thedata accessed (e.g., data in database 116 or storage platform 122).Subsequent or concurrent queries accessing the same table file willtherefore be performed on the same node, according to some exampleembodiments.

As discussed, the nodes and virtual warehouses may change dynamically inresponse to environmental conditions (e.g., disaster scenarios),hardware/software issues (e.g., malfunctions), or administrative changes(e.g., changing from a large cluster to smaller cluster to lower costs).In some example embodiments, when the set of nodes changes, no data isreshuffled immediately. Instead, the LRU replacement policy isimplemented to eventually replace the lost cache contents over multiplejobs. Thus, the caches reduce or eliminate the bottleneck problemsoccurring in platforms that consistently retrieve data from remotestorage systems. Instead of repeatedly accessing data from the remotestorage devices, the systems and methods described herein access datafrom the caches in the execution nodes, which is significantly fasterand avoids the bottleneck problem discussed above. In some embodiments,the caches are implemented using high-speed memory devices that providefast access to the cached data. Each cache can store data from any ofthe storage devices in the cloud computing storage platform 104.

Further, the cache resources and computing resources may vary betweendifferent execution nodes. For example, one execution node may containsignificant computing resources and minimal cache resources, making theexecution node useful for tasks that require significant computingresources. Another execution node may contain significant cacheresources and minimal computing resources, making this execution nodeuseful for tasks that require caching of large amounts of data. Yetanother execution node may contain cache resources providing fasterinput/output (I/O) operations, useful for tasks that require fastscanning of large amounts of data. In some embodiments, the executionplatform 114 implements skew handling to distribute work amongst thecache resources and computing resources associated with a particularexecution, where the distribution may be further based on the expectedtasks to be performed by the execution nodes. For example, an executionnode may be assigned more processing resources if the tasks performed bythe execution node become more processor-intensive. Similarly, anexecution node may be assigned more cache resources if the tasksperformed by the execution node require a larger cache capacity.Further, some nodes may be executing much slower than others due tovarious issues (e.g., virtualization issues, network overhead). In someexample embodiments, the imbalances are addressed at the scan levelusing a file stealing scheme. In particular, whenever a node processcompletes scanning its set of input files, it requests additional filesfrom other nodes. If one of the other nodes receives such a request, thenode analyzes its own set (e.g., how many files are left in the inputfile set when the request is received), and then transfers ownership ofone or more of the remaining files for the duration of the current job(e.g., query). The requesting node (e.g., the file stealing node) thenreceives the data (e.g., header data) and downloads the files from thecloud computing storage platform 104 (e.g., from data storage device124-1), and does not download the files from the transferring node. Inthis way, lagging nodes can transfer files via file stealing in a waythat does not worsen the load on the lagging nodes.

Although virtual warehouses 1, 2, and n are associated with the sameexecution platform 114, the virtual warehouses may be implemented usingmultiple computing systems at multiple geographic locations. Forexample, virtual warehouse 1 can be implemented by a computing system ata first geographic location, while virtual warehouses 2 and n areimplemented by another computing system at a second geographic location.In some embodiments, these different computing systems are cloud-basedcomputing systems maintained by one or more different entities.

Additionally, each virtual warehouse is shown in FIG. 3 as havingmultiple execution nodes. The multiple execution nodes associated witheach virtual warehouse may be implemented using multiple computingsystems at multiple geographic locations. For example, an instance ofvirtual warehouse 1 implements execution nodes 302-1 and 302-2 on onecomputing platform at a geographic location and implements executionnode 302-n at a different computing platform at another geographiclocation. Selecting particular computing systems to implement anexecution node may depend on various factors, such as the level ofresources needed for a particular execution node (e.g., processingresource requirements and cache requirements), the resources availableat particular computing systems, communication capabilities of networkswithin a geographic location or between geographic locations, and whichcomputing systems are already implementing other execution nodes in thevirtual warehouse.

Execution platform 114 is also fault tolerant. For example, if onevirtual warehouse fails, that virtual warehouse is quickly replaced witha different virtual warehouse at a different geographic location.

A particular execution platform 114 may include any number of virtualwarehouses. Additionally, the number of virtual warehouses in aparticular execution platform is dynamic, such that new virtualwarehouses are created when additional processing and/or cachingresources are needed. Similarly, existing virtual warehouses may bedeleted when the resources associated with the virtual warehouse are nolonger necessary.

In some embodiments, the virtual warehouses may operate on the same datain cloud computing storage platform 104, but each virtual warehouse hasits own execution nodes with independent processing and cachingresources. This configuration allows requests on different virtualwarehouses to be processed independently and with no interferencebetween the requests. This independent processing, combined with theability to dynamically add and remove virtual warehouses, supports theaddition of new processing capacity for new users without impacting theperformance observed by the existing users.

FIG. 4 shows an example cluster computing dataflow architecture 400implementing the cluster diagnostic system, according to some exampleembodiments. In the example illustrated in FIG. 4, an Apache Sparkcluster computing system is discussed as an example, although it isappreciated that other cluster computing systems can likewise beimplemented to perform cluster diagnostics using the cluster diagnosticsystem. Generally, Spark is a driver/executor model cluster that runs auser's application. This driver and executor can be implemented as asingle logical unit to which a job or an application is distributed.Each spark executor can be implemented as an isolated java virtualmachine (JVM) process serving one spark application/job only, accordingto some example embodiments. The spark executors are terminated when thespark application or job is complete. In some example embodiments wherethe cluster diagnostic system has enabled logging for the spark job orapplication, the logging files will reside on the machine in which theexecutor runs (e.g., within the network or compute instancesimplementing the cluster), according to some example embodiments.

In addition to the database API connector 145 (e.g., JDBC), in someexample embodiments, a network-based data warehouse system implements aSpark connector on each of the driver and executor nodes in the cluster.The Spark connector can be implemented as a plug-in for Apache Spark toread from the network-based data warehouse system or write to one ormore databases of the network-based data warehouse system. In someexample embodiments, the Spark connector is a plug-in that is agnosticto whether it is implemented in an executor or a driver; that is, forexample, the plug-in can include libraries or sets of instructions thatenable the plug-in to function and perform the same actions whether runfrom an executor node or driver node.

With reference to FIG. 4, the Spark user 405 can connect to the Sparkcluster to write or create an application (e.g., a Spark job), and canthen submit the application to the Spark cluster for execution. Thespark driver 410 receives the application and invokes a spark connectordriver 425 to implement the actual read and write job for database datafrom the network-based data warehouse system 445 provided through thedatabase connector 440.

The read and write job is split into multiple small process units calledpartitions (tasks). The spark driver 410 distributes the tasks to one ormore spark executors 420 via a broadcast framework 415. Each sparkexecutor receives one task at a time and processes the task, followed byreceiving a next task from the broadcast framework 415, until all tasksare processed (e.g., all tasks are processed by the multiple executorsspawned for a given spark job).

In some example embodiments, when the spark user 405 encounters anapplication or job issue (e.g., one or more errors encountered in thedriver or executors), the spark user 405 then reproduces the issue withan additional cluster diagnosis parameter in the application/job set totrue (e.g., “enable_diagnosis=true”, where the default setting is falseor inactive). The spark driver 410 then receives the application withthe diagnosis setting now set to true. The spark driver 410 parses theapplication and identifies the diagnosis setting as now set to true, andin response retrieves telemetry metadata 435 from the network-based datawarehouse system 445. The telemetry metadata comprises credential oraccess authentication data (e.g., JDBC session token, and/or additionalcredential/access data) for communicating with the telemetry service ofthe network-based data warehouse system (e.g., an electronic messagingservice communicating with servers over a rest API). In some exampleembodiments, the spark connector driver 425 generates logging data thatis sent as telemetry message 427, which includes logging and telemetrymetadata for access to the network-based data warehouse system 445.

Further, the spark driver 410 passes the telemetry metadata to the sparkconnector executors 430 via the broadcast framework 415 and the sparkexecutors 420 when the diagnostic setting is set to true. For each ofthe spark connector executors, if the setting is set to true, each ofthe spark connector executors enables logging to be collected and sentto the telemetry service of the network-based data warehouse system 445.For example, the telemetry messages for a given spark connector executor430 can be sent as the telemetry message 450, which includes both thelogging and the telemetry metadata for access to the network-based datawarehouse system 445.

In some example embodiments, both the spark connector driver 425 and thespark connector executor 430 send spark connector log data (as telemetrymessage data) to the telemetry service of the network-based datawarehouse system 445, instead of the internal or native logging systemof Spark such as LOG 4J. In some example embodiments, a wrapper for thelogger object is introduced. If the cluster diagnosis setting for agiven job is enabled, the wrapper is configured to pass the log requestto the logger object, and then send the log entry as a telemetry servicemessage to the network-based data warehouse system 445 using thetelemetry metadata. The telemetry metadata and credentials are used toaccess the telemetry network service and send telemetry messages to thenetwork-based data warehouse system 445 (e.g., over a network,REST-API). Accordingly, the spark driver 410 retrieves the telemetrymetadata 435 through the database connector 440 for distribution to theexecutors to transmit the telemetry messages from the executors.

FIG. 5 shows a flow diagram of a method 500 for implementing a clusterdiagnosis system, according to some example embodiments. At operation505, an error in the application is encountered. For example, one ormore issues of an application or job submitted by a spark user to thecluster computing system is encountered. At operation 510, the sparkuser sets the cluster telemetry analysis setting to true in theapplication or job. At operation 515, the application is resubmitted tothe cluster computing environment. At operation 520, the driverretrieves the telemetry metadata (e.g., session token, credentials) fromthe database system (e.g., via the database JDBC connector). Atoperation 525, the driver distributes the telemetry metadata to thecluster executors. At operation 530, the executors perform one or morejob tasks for the application. At operation 535, telemetry message datawith log data accompanied by telemetry metadata for access to thenetwork database is transmitted to the database system by the cluster(e.g., driver data from the driver, executor data from one or more ofthe executors), according to some example embodiments. For example, atoperation 535, the spark connector driver 425 transmits the telemetrymessage 427 to the network-based data warehouse system 445; further, atoperation 535, the spark connector executor 430 transmits telemetrymessage 450 to the network-based data warehouse system 445.

In some example embodiments, each cluster application (e.g., Spark job)has a unique ID (e.g., spark application ID). In some exampleembodiments, each telemetry message sent to the telemetry service caninclude the unique ID. The telemetry message data can be stored in acredible data store for the specific spark job. In some exampleembodiments, each telemetry message further includes metadata:

spark_application_id: (value),execution_id: (value),executor_id: (value),partition_id: (value),sequencer (value),log_level: trace,log_message: logging entry

FIG. 6 illustrates a diagrammatic representation of a machine 600 in theform of a computer system within which a set of instructions may beexecuted for causing the machine 600 to perform any one or more of themethodologies discussed herein, according to an example embodiment.Specifically, FIG. 6 shows a diagrammatic representation of the machine600 in the example form of a computer system, within which instructions616 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 600 to perform any one ormore of the methodologies discussed herein may be executed. For example,the instructions 616 may cause the machine 600 to execute any one ormore operations of any of the dataflows and/or methods discussed above.In this way, the instructions 616 transform a general, non-programmedmachine into a particular machine 600 (e.g., the user device 106, theaccess management system 110, the compute service manager 112, theexecution platform 114, the access management system 118, the cloudinterface 120) that is specially configured to carry out any one of thedescribed and illustrated functions in the manner described herein.

In alternative embodiments, the machine 600 operates as a standalonedevice or may be coupled (e.g., networked) to other machines. In anetworked deployment, the machine 600 may operate in the capacity of aserver machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 600 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a smart phone, a mobiledevice, a network router, a network switch, a network bridge, or anymachine capable of executing the instructions 616, sequentially orotherwise, that specify actions to be taken by the machine 600. Further,while only a single machine 600 is illustrated, the term “machine” shallalso be taken to include a collection of machines 600 that individuallyor jointly execute the instructions 616 to perform any one or more ofthe methodologies discussed herein.

The machine 600 includes processors 610, memory 630, and I/O components650 configured to communicate with each other such as via a bus 602. Inan example embodiment, the processors 610 (e.g., a central processingunit (CPU), a reduced instruction set computing (RISC) processor, acomplex instruction set computing (CISC) processor, a graphicsprocessing unit (GPU), a digital signal processor (DSP), anapplication-specific integrated circuit (ASIC), a radio-frequencyintegrated circuit (RFIC), another processor, or any suitablecombination thereof) may include, for example, a processor 612 and aprocessor 614 that may execute the instructions 616. The term“processor” is intended to include multi-core processors 610 that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions 616 contemporaneously. AlthoughFIG. 6 shows multiple processors 610, the machine 600 may include asingle processor with a single core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiple cores, or any combinationthereof.

The memory 630 may include a main memory 632, a static memory 634, and astorage unit 636, all accessible to the processors 610 such as via thebus 602. The main memory 632, the static memory 634, and the storageunit 636 store the instructions 616 embodying any one or more of themethodologies or functions described herein. The instructions 616 mayalso reside, completely or partially, within the main memory 632, withinthe static memory 634, within the storage unit 636, within at least oneof the processors 610 (e.g., within the processor's cache memory), orany suitable combination thereof, during execution thereof by themachine 600.

The I/O components 650 include components to receive input, provideoutput, produce output, transmit information, exchange information,capture measurements, and so on. The specific I/O components 650 thatare included in a particular machine 600 will depend on the type ofmachine. For example, portable machines such as mobile phones willlikely include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 650 mayinclude many other components that are not shown in FIG. 6. The I/Ocomponents 650 are grouped according to functionality merely forsimplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 650 mayinclude output components 652 and input components 654. The outputcomponents 652 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), other signal generators, and soforth. The input components 654 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 650 may include communication components 664 operableto couple the machine 600 to a network 660 or devices 661 via a coupling667 and a coupling 663, respectively. For example, the communicationcomponents 664 may include a network interface component or anothersuitable device to interface with the network. In further examples, thecommunication components 664 may include wired communication components,wireless communication components, cellular communication components,and other communication components to provide communication via othermodalities. The devices 661 may be another machine or any of a widevariety of peripheral devices (e.g., a peripheral device coupled via auniversal serial bus (USB)). For example, as noted above, the machine600 may correspond to any one of the user device 106, the accessmanagement system 110, the compute service manager 112, the executionplatform 114, the access management system 118, the cloud interface 120.

The various memories (e.g., 630, 632, 634, and/or memory of theprocessor(s) 610 and/or the storage unit 636) may store one or more setsof instructions 616 and data structures (e.g., software) embodying orutilized by any one or more of the methodologies or functions describedherein. These instructions 616, when executed by the processor(s) 610,cause various operations to implement the disclosed embodiments.

As used herein, the terms “machine-storage medium,” “device-storagemedium,” and “computer-storage medium” mean the same thing and may beused interchangeably in this disclosure. The terms refer to a single ormultiple storage devices and/or media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storeexecutable instructions and/or data. The terms shall accordingly betaken to include, but not be limited to, solid-state memories, andoptical and magnetic media, including memory internal or external toprocessors. Specific examples of machine-storage media, computer-storagemedia, and/or device-storage media include non-volatile memory,including by way of example semiconductor memory devices, e.g., erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), field-programmable gate arrays(FPGAs), and flash memory devices; magnetic disks such as internal harddisks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The terms “machine-storage media,” “computer-storage media,” and“device-storage media” specifically exclude carrier waves, modulateddata signals, and other such media, at least some of which are coveredunder the term “signal medium” discussed below.

In various example embodiments, one or more portions of the network 660may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local-area network (LAN), a wireless LAN (WLAN), awide-area network (WAN), a wireless WAN (WWAN), a metropolitan-areanetwork (MAN), the Internet, a portion of the Internet, a portion of thepublic switched telephone network (PSTN), a plain old telephone service(POTS) network, a cellular telephone network, a wireless network, aWi-Fi® network, another type of network, or a combination of two or moresuch networks. For example, the network 660 or a portion of the network660 may include a wireless or cellular network, and the coupling may bea Code Division Multiple Access (CDMA) connection, a Global System forMobile communications (GSM) connection, or another type of cellular orwireless coupling. In this example, the coupling may implement any of avariety of types of data transfer technology, such as Single CarrierRadio Transmission Technology (1×RTT), Evolution-Data Optimized (EVDO)technology, General Packet Radio Service (GPRS) technology, EnhancedData rates for GSM Evolution (EDGE) technology, third GenerationPartnership Project (3GPP) including 3G, fourth generation wireless (4G)networks, Universal Mobile Telecommunications System (UMTS), High-SpeedPacket Access (HSPA), Worldwide Interoperability for Microwave Access(WiMAX), Long Term Evolution (LTE) standard, others defined by variousstandard-setting organizations, other long-range protocols, or otherdata transfer technology.

The instructions 616 may be transmitted or received over the network 660using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components664) and utilizing any one of a number of well-known transfer protocols(e.g., HTTP). Similarly, the instructions 616 may be transmitted orreceived using a transmission medium via a coupling (e.g., apeer-to-peer coupling) to the devices 661. The terms “transmissionmedium” and “signal medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms “transmission medium” and“signal medium” shall be taken to include any intangible medium that iscapable of storing, encoding, or carrying the instructions 616 forexecution by the machine 600, and include digital or analogcommunications signals or other intangible media to facilitatecommunication of such software. Hence, the terms “transmission medium”and “signal medium” shall be taken to include any form of modulated datasignal, carrier wave, and so forth. The term “modulated data signal”means a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in the signal.

The terms “machine-readable medium,” “computer-readable medium,” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms are defined to includeboth machine-storage media and transmission media. Thus, the termsinclude both storage devices/media and carrier waves/modulated datasignals.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Similarly, the methods described hereinmay be at least partially processor-implemented. For example, at leastsome of the operations of the FIG. 4 and FIG. 5 may be performed by oneor more processors. The performance of certain of the operations may bedistributed among the one or more processors, not only residing within asingle machine, but also deployed across a number of machines. In someexample embodiments, the processor or processors may be located in asingle location (e.g., within a home environment, an office environment,or a server farm), while in other embodiments the processors may bedistributed across a number of locations.

Although the embodiments of the present disclosure have been describedwith reference to specific example embodiments, it will be evident thatvarious modifications and changes may be made to these embodimentswithout departing from the broader scope of the inventive subjectmatter. Accordingly, the specification and drawings are to be regardedin an illustrative rather than a restrictive sense. The accompanyingdrawings that form a part hereof show, by way of illustration, and notof limitation, specific embodiments in which the subject matter may bepracticed. The embodiments illustrated are described in sufficientdetail to enable those skilled in the art to practice the teachingsdisclosed herein. Other embodiments may be used and derived therefrom,such that structural and logical substitutions and changes may be madewithout departing from the scope of this disclosure. This DetailedDescription, therefore, is not to be taken in a limiting sense, and thescope of various embodiments is defined only by the appended claims,along with the full range of equivalents to which such claims areentitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent, to those of skill inthe art, upon reviewing the above description.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended; that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim is still deemed to fall within thescope of that claim.

The following are example embodiments:

Example 1. A method comprising: receiving, by a computing cluster, anapplication for processing by nodes of the computing cluster, the nodesincluding a driver node and a plurality of execution nodes forprocessing of tasks of the application; distributing, by the drivernode, the tasks of the application for processing by the plurality ofexecution nodes; processing the tasks by the plurality of executionnodes; identifying, by the computing cluster, one or more errors in theapplication received by the computing cluster; in response to the one ormore errors, receiving, by the driver node, telemetry metadata foraccess to a telemetry network service of a distributed database thatprovides data to the computing cluster through a database connector, thedriver node receiving the telemetry metadata through the databaseconnector of the distributed database; and distributing, by the drivernode, the telemetry metadata to the plurality of execution nodes forre-processing.

Example 2. The method of example 1, further comprising: re-processing,by the plurality of execution nodes, the tasks of the application; andtransmitting, by the plurality of execution nodes, to the telemetrynetwork service of the distributed database, log data that is generatedby the plurality of execution nodes while re-processing the tasks of theapplication.

Example 3. The method of any of examples 1 or 2, wherein the log data istransmitted to the telemetry network service by one or more databaseconnectors integrated in one or more of the plurality of executionnodes.

Example 4. The method of any of examples 1-3, further comprising:transmitting, by the driver node, to the telemetry network service ofthe distributed database, additional log data that is generated by thedriver node while processing the application.

Example 5. The method of any of examples 1-4, wherein the additional logdata is transmitted to the telemetry network service by a driverdatabase connector integrated in the driver node.

Example 6. The method of any of examples 1-5, further comprising:determining, by the driver node, that a telemetry setting is active inthe application.

Example 7. The method of any of examples 1-6, further comprising: inresponse to the telemetry setting being active, bypassing a nativelogging service of the computing cluster using a wrapper.

Example 8. The method of any of examples 1-7, further comprising: inresponse to the telemetry setting being active, passing a log request toa logger object of the wrapper.

Example 9. The method of any of examples 1-8, wherein the logger objecttransmits the log data to the telemetry network service of thedistributed database using the telemetry metadata for access to atelemetry network service.

Example 10. The method of any of examples 1-9, wherein the telemetrysetting is set by default in the application to inactive and log data isnot transmitted to the telemetry network service by the computingcluster while the telemetry setting is set to inactive.

Example 11. The method of any of examples 1-10, wherein the telemetrynetwork service is an electronic message network service of one or moreservers of the distributed database.

Example 12. A system comprising: one or more processors of a machine;and a memory storing instructions that, when executed by the one or moreprocessors, cause the machine to perform operations comprising:receiving, by a computing cluster, an application for processing bynodes of the computing cluster, the nodes including a driver node and aplurality of execution nodes for processing of tasks of the application;distributing, by the driver node, the tasks of the application forprocessing by the plurality of execution nodes; processing the tasks bythe plurality of execution nodes; identifying, by the computing cluster,one or more errors in the application received by the computing cluster;in response to the one or more errors, receiving, by the driver node,telemetry metadata for access to a telemetry network service of adistributed database that provides data to the computing cluster througha database connector, the driver node receiving the telemetry metadatathrough the database connector of the distributed database; anddistributing, by the driver node, the telemetry metadata to theplurality of execution nodes for purposes of re-processing.

Example 13. The system of example 12, the operations further comprising:re-processing, by the plurality of execution nodes, the tasks of theapplication; and transmitting, by the plurality of execution nodes, tothe telemetry network service of the distributed database, log data thatis generated by the plurality of execution nodes while re-processing thetasks of the application.

Example 14. The system of any of examples 12 or 13, wherein the log datais transmitted to the telemetry network service by one or more databaseconnectors integrated in one or more of the plurality of executionnodes.

Example 15. The system of any of examples 12-14, the operations furthercomprising: transmitting, by the driver node, to the telemetry networkservice of the distributed database, additional log data that isgenerated by the driver node while processing the application.

Example 16. The system of any of examples 12-15, wherein the additionallog data is transmitted to the telemetry network service by a driverdatabase connector integrated in the driver node.

Example 17. The system of any of examples 12-16, the operations furthercomprising: determining, by the driver node, that a telemetry setting isactive in the application.

Example 18. The system of any of examples 12-17, the operations furthercomprising: in response to the telemetry setting being active, bypassinga native logging service of the computing cluster using a wrapper.

Example 19. A machine storage medium embodying instructions that, whenexecuted by a machine, cause the machine to perform operationscomprising: receiving, by a computing cluster, an application forprocessing by nodes of the computing cluster, the nodes including adriver node and a plurality of execution nodes for processing of tasksof the application; distributing, by the driver node, the tasks of theapplication for processing by the plurality of execution nodes;processing the tasks by the plurality of execution nodes; identifying,by the computing cluster, one or more errors in the application receivedby the computing cluster; in response to the one or more errors,receiving, by the driver node, telemetry metadata for access to atelemetry network service of a distributed database that provides datato the computing cluster through a database connector, the driver nodereceiving the telemetry metadata through the database connector of thedistributed database; and distributing, by the driver node, thetelemetry metadata to the plurality of execution nodes forre-processing.

Example 20. The machine storage medium of example 19, the operationsfurther comprising: re-processing, by the plurality of execution nodes,the tasks of the application; and transmitting, by the plurality ofexecution nodes, to the telemetry network service of the distributeddatabase, log data that is generated by the plurality of execution nodeswhile re-processing the tasks of the application.

What is claimed is:
 1. A method comprising: receiving, on a computingcluster, tasks of an application for processing by a plurality of nodesof the computing cluster; identifying telemetry metadata for access to atelemetry service of a database that provides data to the computingcluster; distributing, by one of the plurality of nodes, telemetrymetadata to other nodes of the plurality of nodes; generating log databy processing the tasks of the application; and transmitting the logdata to the database using the other nodes, the other nodes using thetelemetry metadata distributed by the one of the nodes to the othernodes to transmit the log data to the telemetry service of the database.2. The method of claim 1, further comprising: transmitting, by the oneof the nodes, to the telemetry service of the database, additional logdata that is generated by the one of the plurality of nodes.
 3. Themethod of claim 2, wherein the one of the nodes generates the additionallog data from processing the tasks of the application.
 4. The method ofclaim 2, wherein the additional log data is transmitted from the one ofthe plurality of nodes to the telemetry service by a database connector.5. The method of claim 1, wherein the log data is transmitted from theother nodes of the plurality of nodes to the telemetry service by adatabase connector.
 6. The method of claim 1, wherein the other nodes ofthe plurality of nodes generate the log data from processing the tasksof the application.
 7. The method of claim 1, wherein the telemetryservice is an electronic message service of the database.
 8. A systemcomprising: one or more processors of a machine; and at least one memorystoring instructions that, when executed by the one or more processors,cause the machine to perform operations comprising: receiving, on acomputing cluster, tasks of an application for processing by a pluralityof nodes of the computing cluster; identifying telemetry metadata foraccess to a telemetry service of a database that provides data to thecomputing cluster; distributing, by one of the plurality of nodes,telemetry metadata to other nodes of the plurality of nodes; generatinglog data by processing the tasks of the application; and transmittingthe log data to the database using the other nodes, the other nodesusing the telemetry metadata distributed by the one of the nodes to theother nodes to transmit the log data to the telemetry service of thedatabase.
 9. The system of claim 8, the operations further comprising:transmitting, by the one of the nodes, to the telemetry service of thedatabase, additional log data that is generated by the one of theplurality of nodes.
 10. The system of claim 9, wherein the one of thenodes generates the additional log data from processing the tasks of theapplication.
 11. The system of claim 9, wherein the additional log datais transmitted from the one of the plurality of nodes to the telemetryservice by a database connector.
 12. The system of claim 8, wherein thelog data is transmitted from the other nodes of the plurality of nodesto the telemetry service by a database connector.
 13. The system ofclaim 8, wherein the other nodes of the plurality of nodes generate thelog data from processing the tasks of the application.
 14. The system ofclaim 8, wherein the telemetry service is an electronic message serviceof the database.
 15. A non-transitory machine storage medium embodyinginstructions that, when executed by a machine, cause the machine toperform operations comprising: receiving, on a computing cluster, tasksof an application for processing by a plurality of nodes of thecomputing cluster; identifying telemetry metadata for access to atelemetry service of a database that provides data to the computingcluster; distributing, by one of the plurality of nodes, telemetrymetadata to other nodes of the plurality of nodes; generating log databy processing the tasks of the application; and transmitting the logdata to the database using the other nodes, the other nodes using thetelemetry metadata distributed by the one of the nodes to the othernodes to transmit the log data to the telemetry service of the database.16. The non-transitory machine storage medium of claim 15, furthercomprising: transmitting, by the one of the nodes, to the telemetryservice of the database, additional log data that is generated by theone of the plurality of nodes.
 17. The non-transitory machine storagemedium of claim 16, wherein the one of the nodes generates theadditional log data from processing the tasks of the application. 18.The non-transitory machine storage medium of claim 16, wherein theadditional log data is transmitted from the one of the plurality ofnodes to the telemetry service by a database connector.
 19. Thenon-transitory machine storage medium of claim 15, wherein the log datais transmitted from the other nodes of the plurality of nodes to thetelemetry service by a database connector.
 20. The non-transitorymachine storage medium of claim 15, wherein the other nodes of theplurality of nodes generate the log data from processing the tasks ofthe application.